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| Visual Exploration of Network Traffic for Host and Server Monitoring: The screenshot shows the hourly amount of network traffic for thousands of hosts in a large computer network for 24 hours. The different nested circles represent the topological subnet hierarchy of the network. Each filled circle represents a whole subnet or when zoomed in single hosts. Each circle consists of 24 segments, while each colored segment visualizes the number of bytes transferred in the respective hour. |
Never before has data been generated and collected at such high
volumes as it is today. As the volumes of multidimensional data
available to businesses, scientists, and the public increase, their
effective use becomes more challenging. Visual analytics seeks to
provide people with effective ways to understand and analyze large
multidimensional data sets, while also enabling them to act upon their
findings immediately.
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| Visualization of Large-Scale Distributed Network Attacks: This picture illustrates attacks from the Internet to computers located in a large computer network (brute force SSH attacks). The background represents the network structure with computer systems as rectangles. External hosts are shown as colored circles on the outside. The splines represent the connections between attackers and computers within the network. This reveals a network scan (from top) and a distributed attack (from bottom) originating from hundreds of hosts working together in an attempt to break into specific computer systems. |
It integrates the analytic capabilities of the
computer and the abilities of the human analyst, allowing novel
discoveries and empowering individuals to take control of the analytical
process.
This talk presents the potential of visual analytics and
discusses the role of automated versus interactive visual techniques in
dealing with big data. A variety of application examples ranging from
news analysis over network security to SC performance analysis
illustrate not only the exiting potential of visual analysis techniques, but also
their limitations.
Speaker background:
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| Dr. Daniel A. Keim |
Dr. Daniel A. Keim is professor and head of the Information Visualization and Data Analysis Research Group in the Computer Science Department of the University of Konstanz, Germany. He has been actively involved in data analysis and information visualization research for more than 20 years and developed a number of novel visual analysis techniques for very large data sets.
He has been program co-chair of the IEEE InfoVis and IEEE VAST as well as the ACM SIGKDD conference, and he is member of the IEEE VAST as well as EuroVis steering committees. He is coordinator of the German Science Foundation funded Strategic Research Initiative “Scalable Visual Analytics” and has been scientific coordinator of the European Commission funded Coordination Action “Visual Analytics – Mastering the Information Age (VisMaster)”.
Dr. Keim received his Ph.D. and habilitation degrees in computer science from the University of Munich. Before joining the University of Konstanz, Dr. Keim was associate professor at the University of Halle, Germany, and Senior Technology Consultant at AT&T Shannon Research Labs, NJ, USA.
